Triple
T20380457
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Samuel Wake |
E497810
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Samuel Wake |
—
|
NE NERFINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Samuel Wake | Statement: [Samuel Wake, name, Samuel Wake]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Samuel Wake Context triple: [Samuel Wake, name, Samuel Wake]
-
A.
Samuel Wake
chosen
Samuel Wake was a British sea captain after whom the remote Pacific atoll Wake Island was named.
-
B.
Thomas Wake
Thomas Wake is a grizzled, domineering lighthouse keeper in the psychological horror film "The Lighthouse," known for his superstitious beliefs and volatile relationship with his younger assistant.
-
C.
Samuel Toombs
Samuel Toombs was a British Labour Party politician who served as Member of Parliament for the Barnsley constituency in the early 20th century.
-
D.
William Waynfleet
William Waynfleet was a 15th-century English bishop, Lord Chancellor, and founder of Magdalen College, Oxford.
-
E.
Samuel Dale
Samuel Dale was an American frontiersman, soldier, and politician known for his role in the Creek War and early Alabama history.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69e0b4a5b7908190a972e4e7e698ae94 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e678b026e081909541e545886c8380 |
completed | April 20, 2026, 7:04 p.m. |
Created at: April 16, 2026, 11:27 a.m.